Deep learning and process understanding for data-driven Earth system science

Markus Reichstein (), Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler, Nuno Carvalhais and Prabhat
Additional contact information
Markus Reichstein: Max Planck Institute for Biogeochemistry
Gustau Camps-Valls: Image Processing Laboratory (IPL), University of València
Bjorn Stevens: Max Planck Institute for Meteorology
Martin Jung: Max Planck Institute for Biogeochemistry
Joachim Denzler: Michael-Stifel-Center Jena for Data-driven and Simulation Science
Nuno Carvalhais: Max Planck Institute for Biogeochemistry
Prabhat: National Energy Research Supercomputing Center, Lawrence Berkeley National Laboratory

Nature, 2019, vol. 566, issue 7743, 195-204

Abstract: Abstract Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

Date: 2019
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DOI: 10.1038/s41586-019-0912-1

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